What problem does it solve?
This Skill addresses critical performance bottlenecks in AgentDB vector databases, such as high memory consumption and slow search speeds, especially when scaling to millions of vectors. It ensures your AI applications remain fast and efficient.
Core Features & Use Cases
- Quantization Strategies: Reduce memory usage by 4-32x (e.g., binary, scalar, product quantization) while maintaining accuracy.
- HNSW Indexing: Achieve 150-12,500x faster vector search with Hierarchical Navigable Small World indexing.
- Caching Strategies: Implement in-memory pattern caching for sub-millisecond retrieval of frequently accessed data.
- Batch Operations: Speed up data ingestion with 500x faster batch inserts.
- Use Case: Optimize a large-scale vector database for a real-time recommendation engine, reducing memory footprint on edge devices and accelerating search results for millions of users.
Quick Start
Run comprehensive performance benchmarks:
npx agentdb@latest benchmark
Enable optimized configuration with binary quantization and a cache:
import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
const adapter = await createAgentDBAdapter({
dbPath: '.agentdb/optimized.db',
quantizationType: 'binary',
cacheSize: 1000,
});
